Soft Computing

, Volume 22, Issue 9, pp 3097–3109 | Cite as

Return scaling cross-correlation forecasting by stochastic time strength neural network in financial market dynamics

  • Haiyan Mo
  • Jun Wang
Methodologies and Application


A return scaling cross-correlation function of exponential parameter is introduced in the present work, and a stochastic time strength neural network model is developed to predict the return scaling cross-correlations between two real stock market indexes, Shanghai Composite Index and Shenzhen Component Index. In the proposed model, the stochastic time strength function gives a weight for each historical data and makes the model have the effect of random movement. The empirical research is performed in testing the model forecasting effect of long-term cross-correlation relationships by training short-term cross-correlations, and a corresponding comparison analysis is made to the backpropagation neural network model. The empirical results show that the proposed neural network is advantageous in increasing the forecasting precision.


Forecast Cross-correlation Return scaling Neural network Stochastic time strength function Financial time series 



The authors were supported in part by National Natural Science Foundation of China Grant No. 71271026.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of ScienceBeijing Jiaotong UniversityBeijingPeople’s Republic of China

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